The hallmark feature of episodic memory is the ability to link events with their temporal and situational contexts. This ability allows for memories to be truly autobiographical, and failures of episodic memory are signs of normal aging and neurodegenerative disease. The proposed research aims to illuminate the neural and cognitive mechanisms underlying human episodic (contextually-mediated) memory through both computational modeling and the analysis of electrocorticographic and single neuron recordings taken as neurosurgical patients search their memory for recently studied material. Building on prior retrieved context models of episodic memory, Aim 1 is to develop an attractor neural network (NeuroCMR) in which both remote and recent memories are stored by associating item representations with unique contextual states that gradually evolve as a function of the sequence of experienced and recalled items. Searching memory for a given item is influenced not only by the contextual information associated with that target but also by the multitude of prior memories learned in partially overlapping contexts. Aims 2-4 will test the predictions of NeuroCMR using neural data. Specifically, patterns of electrocorticographic and single-neuron activity will be detected using multivariate pattern analysis methods. These methods will be used to identify the neural signatures of content and context information during both encoding and retrieval, and to identify their anatomical substrates. This work will serve as an important bridge between the behavioral and neurobiological approaches to human memory, and will provide insights into the mechanisms of memory decline both in normal aging and in neurological disease.